Accelerating U.S. Clean Energy Deployment: Investor Policy Priorities

International investment to mitigate climate change is far below levels needed to reach the two-degree target. The International Energy Agency estimates that an average of an additional $1 trillion in incremental financing for clean energy is needed to meet the temperature target. In September 2014, over 350 investors representing$24 trillion in assets issued the Global Investor Statement on Climate Change, calling on governments to create an ambitious global agreement that includes a meaningful price on carbon -- the "Clean Trillion."This paper connects the Clean Trillion goal to the current United States climate and clean energy policy framework, which is a mixture of federal, state, and local initiatives. The paper outlines the 2015 U.S. policy priorities of the Policy Working Group of the Investor Network on Climate Risk (INCR), a network of more than 110 institutional investors primarily based in the U.S., focused on investment risks and opportunities associated with climate change

Definite orthogonal modular forms:Computations, Excursions and Discoveries

We consider spaces of modular forms attached to definite orthogonal groups of low even rank and nontrivial level, equipped with Hecke operators defined by Kneser neighbours. After reviewing algorithms to compute with these spaces, we investigate endoscopy using theta series and a theorem of Rallis. Along the way, we exhibit many examples and pose several conjectures. As a first application, we express counts of Kneser neighbours in terms of coefficients of classical or Siegel modular forms, complementing work of Chenevier-Lannes. As a second application, we prove new instances of Eisenstein congruences of Ramanujan and Kurokawa-Mizumoto type

Blood Pressure Beyond the Clinic: Rethinking a Health Metric for Everyone

ABSTRACT Blood pressure (BP) is typically captured at irregular intervals, mostly in clinic environments. This approach treats BP as a static snapshot for health classification and largely ignores its value as a continuously fluctuating measure. Recognizing that consumers are increasingly capturing health metrics through wearable devices, we explored BP measurement in relation to everyday living through a two-week field study with 34 adults. Based on questionnaires, measurement logs, and interviews, we examined participants' perceptions and attitudes towards BP variability and their associations of BP with aspects of their lives. We found that participants modified their use of BP devices in response to BP variability, made associations with stress, food, and daily routines, and revealed challenges with the design of current BP devices for personal use. We present design recommendations for BP use in everyday contexts and describe strategies for re-framing BP capture and reporting

A Self-Supervised Feature Map Augmentation (FMA) Loss and Combined Augmentations Finetuning to Efficiently Improve the Robustness of CNNs

Deep neural networks are often not robust to semantically-irrelevant changes in the input. In this work we address the issue of robustness of state-of-the-art deep convolutional neural networks (CNNs) against commonly occurring distortions in the input such as photometric changes, or the addition of blur and noise. These changes in the input are often accounted for during training in the form of data augmentation. We have two major contributions: First, we propose a new regularization loss called feature-map augmentation (FMA) loss which can be used during finetuning to make a model robust to several distortions in the input. Second, we propose a new combined augmentations (CA) finetuning strategy, that results in a single model that is robust to several augmentation types at the same time in a data-efficient manner. We use the CA strategy to improve an existing state-of-the-art method called stability training (ST). Using CA, on an image classification task with distorted images, we achieve an accuracy improvement of on average 8.94% with FMA and 8.86% with ST absolute on CIFAR-10 and 8.04% with FMA and 8.27% with ST absolute on ImageNet, compared to 1.98% and 2.12%, respectively, with the well known data augmentation method, while keeping the clean baseline performance.Comment: Accepted at ACM CSCS 2020 (8 pages, 4 figures

Flight Testing of Guidance, Navigation and Control Systems on the Mighty Eagle Robotic Lander Testbed

During 2011 a series of progressively more challenging flight tests of the Mighty Eagle autonomous terrestrial lander testbed were conducted primarily to validate the GNC system for a proposed lunar lander. With the successful completion of this GNC validation objective the opportunity existed to utilize the Mighty Eagle as a flying testbed for a variety of technologies. In 2012 an Autonomous Rendezvous and Capture (AR&C) algorithm was implemented in flight software and demonstrated in a series of flight tests. In 2012 a hazard avoidance system was developed and flight tested on the Mighty Eagle. Additionally, GNC algorithms from Moon Express and a MEMs IMU were tested in 2012. All of the testing described herein was above and beyond the original charter for the Mighty Eagle. In addition to being an excellent testbed for a wide variety of systems the Mighty Eagle also provided a great learning opportunity for many engineers and technicians to work a flight program

Particle Physics Implications for CoGeNT, DAMA, and Fermi

Recent results from the CoGeNT collaboration (as well as the annual modulation reported by DAMA/LIBRA) point toward dark matter with a light (5-10 GeV) mass and a relatively large elastic scattering cross section with nucleons (\sigma ~ 10^{-40} cm^2). In order to possess this cross section, the dark matter must communicate with the Standard Model through mediating particles with small masses and/or large couplings. In this Letter, we explore with a model independent approach the particle physics scenarios that could potentially accommodate these signals. We also discuss how such models could produce the gamma rays from the Galactic Center observed in the data of the Fermi Gamma Ray Space Telescope. We find multiple particle physics scenarios in which each of these signals can be accounted for, and in which the dark matter can be produced thermally in the early Universe with an abundance equal to the measured cosmological density.Comment: 4 pages, 2 figure

Machine Learning Prediction of Critical Cooling Rate for Metallic Glasses From Expanded Datasets and Elemental Features

We use a random forest model to predict the critical cooling rate (RC) for glass formation of various alloys from features of their constituent elements. The random forest model was trained on a database that integrates multiple sources of direct and indirect RC data for metallic glasses to expand the directly measured RC database of less than 100 values to a training set of over 2,000 values. The model error on 5-fold cross validation is 0.66 orders of magnitude in K/s. The error on leave out one group cross validation on alloy system groups is 0.59 log units in K/s when the target alloy constituents appear more than 500 times in training data. Using this model, we make predictions for the set of compositions with melt-spun glasses in the database, and for the full set of quaternary alloys that have constituents which appear more than 500 times in training data. These predictions identify a number of potential new bulk metallic glass (BMG) systems for future study, but the model is most useful for identification of alloy systems likely to contain good glass formers, rather than detailed discovery of bulk glass composition regions within known glassy systems

Nondestructive computed tomography for pit inspections

Objective is to develop new approaches to electronically capture digital radiography and computed tomography images at high x-ray energies to satisfy spatial and contrast requirements for inspection of high-density weapons components